Introduction
to Game Theory (II)
Given a 2x2 game
without a saddle-point, an optimal Mixed Strategy
for each player can be calculated. The generalized
solution of M x N games requires Matrix
Algebra: for 2x2 games the solution can be
calculated arithmetically.
For each Blue
row, subtract
Red Strategy 2 from Red Strategy
1, then transpose the absolute differences
of the rows. The procedure for Red
is the same, but calculated from column
differences. These marginal values
(oddments) give the optimal ratio of strategies
(to be mixed at random) for Blue
to obtain the maximum Value of the
game, and for Red to
minimize the loss. This Value is
calculated as the sum of the oddment
for any row times the payoff for each cell, all
divided by the sum of the ratios (oddments).
As shown for Row 1, this is [(1 x 3) + (3 x 5)] / (1 + 3)
= 4.5. The same result should be obtained from any
column or row in the matrix. This algorithm can be applied
to games of greater
interest.
Any departure by either player
from the optimal mix of strategies will result in a net
gain to Blue or a
net loss to Red.
Such zero-sum games are "unfair"
to Red,
as the mean payoff per game to Blue is
4.5. The game can be made "fair"
by subtracting 4.5 from each element of the payoff
matrix, or by having Blue make a side-payment of 4.5
to Red on
each play of the game. Note that the 2:2
ratio for Red is
reducible to 1:1, without changing the payoff of
the game. Matrix algebra is commutative: adding to or
multiplying each cell value by a constant increases the
payoff by the amount added or multiplied, but does not
changed the mixed strategy oddments.
Example from JD Williams (1966); Figure & Text
material © 2021 by Steven M.
Carr